Video Sequence Analysis Using CLIP, Temporal Attention, and RAG

The patent segments the video analysis task into multiple stages: first using CLIP for initial frame-level object detection and classification, then applying temporal attention mechanisms to filter out irrelevant objects across time, and finally using a retrieval-augmented generation system to generate accurate captions. This segmentation allows the system to leverage the speed of foundation models while correcting their inaccuracies through temporal context analysis.

The patent introduces an intermediary retrieval-augmented generation system that acts as a mediator between the CLIP model's initial detections and the final output. This intermediary component retrieves relevant information from a knowledge base and uses it to generate accurate captions, thereby correcting the inaccuracies introduced by direct zero-shot learning while maintaining efficiency.

The patent creates a universal video analysis system that can be deployed across different manufacturing sites without site-specific model retraining. The system uses a multi-functional architecture where CLIP handles general object detection, temporal attention handles background filtering, and the retrieval-augmented generation system handles caption generation. This universal design allows the same system to be deployed horizontally across multiple sites with different production environments.

The patent changes the approach from optimizing model parameters for specific sites to using a parameter-efficient architecture that leverages pre-trained foundation models. By using CLIP's pre-trained weights and adding only lightweight temporal attention layers and retrieval mechanisms, the system achieves high accuracy without requiring extensive retraining or optimization for each specific deployment site.